In the medical imaging field, X-ray computed tomography (CT) provides critical diagnostic information. Recently, CT techniques have been developed to provide quality images at low radiation dose. Patient motion or inaccurate machinery can lead to inaccurate projection angles or distance between the X-ray source and the center of the object being scanned, thereby resulting in inaccurate results. Another challenging problem in this field is geometric calibration, such as for C-arm CT and ultra-high resolution CT. This problem is also related to rigid patient motion compensation, because motion is relative between imaging components and a patient body.
To perform geometric calibration and motion correction, a number of methods have been proposed recently. Analytic methods with a calibration phantom and iterative methods with or without a calibration phantom have been proposed. Analytic methods are widely used in industrial CT and can be based on the identification of elliptical parameters in cone-beam geometry. Some calibration methods are iterative, such as optimization-based calibration for cone-beam CT, self-calibration for cone-beam CT, and self-calibration for cone-beam micro-CT. There is an overlap in the literature on geometric calibration and motion reduction. While some motion reduction methods utilize fast scanning, even with multi-source-detector systems or while avoiding motion-affected data, other methods estimate patient motion and compensate for its effect. Each of these methods has limitations, though.